Method for Bisulfite Sequencing Data Analysis for Whole-Genome Level DNA Methylation Detection in Legumes

Ultrasensitive Detection of Tumor Suppressor Gene Methylation by Piezoelectric Sensing Based on Enrichment of Transcription Activator-Like Effectors

The detection of DNA methylation at cytosine/guanine dinucleotide (CpG) islands in promoter regions of tumor suppressor genes has great potential for early cancer screening, diagnosis, and prognosis monitoring. Nevertheless, achieving accurate, sensitive, cost-effective, and quantitative detection of target methylated DNA remains challenging. Herein, we propose a novel piezoelectric sensor (series piezoelectric quartz crystal (SPQC)) based on transcription activator-like effectors (TALEs) for detecting DNA methylation of Ras association domain family 1 isoform A (RASSF1A) tumor suppressor genes (R-5mC). The sensor employs TALEs-Ni magnetic beads to specifically recognize and separate the R-5mC, thereby improving the detection selectivity. The TALEs-Ni magnetic beads-R-5mC complex is sheared by a nucleic acid enzyme (DNAzyme) to release the single-stranded DNA (ST). ST initiates a catalyzed hairpin assembly (CHA) reaction on the surface of the electrode, which in turn triggers the hybridization chain reaction (HCR) and silver staining for enhanced detection sensitivity. The strategy exhibits a linear response in the detection of R-5mC in the range of 1 fM to 1 nM with a detection limit of 0.79 fM. R-5mC as low as 0.01% can be detected, even in the presence of large numbers of unmethylated DNA. The detection of R-5mC in circulating cell-free DNA (cfDNA) derived from clinical plasma specimens of lung cancer patients yielded satisfactory results.

Targeting the chromatin structural changes of antitumor immunity

Epigenomic imbalance drives abnormal transcriptional processes, promoting the onset and progression of cancer. Although defective gene regulation generally affects carcinogenesis and tumor suppression networks, tumor immunogenicity and immune cells involved in antitumor responses may also be affected by epigenomic changes, which may have significant implications for the development and application of epigenetic therapy, cancer immunotherapy, and their combinations. Herein, we focus on the impact of epigenetic regulation on tumor immune cell function and the role of key abnormal epigenetic processes, DNA methylation, histone post-translational modification, and chromatin structure in tumor immunogenicity, and introduce these epigenetic research methods. We emphasize the value of small-molecule inhibitors of epigenetic modulators in enhancing antitumor immune responses and discuss the challenges of developing treatment plans that combine epigenetic therapy and immunotherapy through the complex interaction between cancer epigenetics and cancer immunology.

Identification of epigenetic methylation-driven signature and risk loci associated with survival for colon cancer

Background

Abnormal methylation is associated with the survival of colon cancer. This study intended to discover a significant model based on methylation-driven genes (MDGs) and screen relative risk loci to assist with determining the prognoses of colon cancer patients.

Methods

We downloaded transcriptome expression profiles and 450K methylation data from the TCGA database. We then collected the two normalized profiles and utilized the MethylMix package to identify a significant signature showing the aberrantly methylated events highly correlated with expression levels. Also, functional enriched pathway analysis based on the ConsensusPathDB database was conducted to further explore the underlying cancer-related crosstalk among the identified MDGs. To find the significant MDGs for prognosis, we applied a univariate Cox regression model, and the hub signature was identified based on the stepwise regression method. A risk model based on MDGs was constructed from the multivariate Cox analysis, and a receiver operating characteristic (ROC) curve was drawn to assess the predictive value of the MDG signature. Additionally, the Kruskal-Wallis (K-W) test was conducted to compare differential distributions of risk scores across groups of clinical variables. Furthermore, the methylation sites relating to the hub genes were screened out and the prognostic genes were searched using the Cox regression method. Last, we carried out gene set enrichment analysis (GSEA) with the risk score levels serving as the phenotype base on the JAVA platform.

Results

A total of 514 colon cancer samples with transcriptome profiles, including 473 tumor samples and 41 matched normal samples, were downloaded. We also obtained 351 methylation profiles comprising 314 tumor samples and 37 normal samples. The 320 MDGs identified by MethylMix were enriched in the generic transcription pathway, RNA polymerase II transcription, activation of SMO, or glutathione metabolism. Furthermore, a 10-MDGs signature was selected as the hub prognostic marker, and the risk model was constructed from the multivariate Cox regression results. We also discovered multiple specific methylated sites that were highly associated with survival. Finally, the GSEA results suggested that several enriched pathways were associated with the identified risk drivers, including extracellular matrix (ECM) receptor interaction, chemokine receptor interaction, and pathways in cancer, as well as calcium signaling pathways.

Conclusions

We conducted a comprehensive investigation of the molecular mechanisms in colon cancer by discovering the risk methylation-driven signature combined with relative methylated sites and constructing a risk model to predict prognosis.